The overall purpose of the Center for End-of-Life Transition Research is to advance the science of care for people facing the end-of-life transition across the life span (e.g., babies born dying, children, adults, and older adults). We will emphasize patient-centered, family-focused respectful death with planning for end-of-life care that is consistent with the patients'and families'values and priorities (e.g., awareness about ways people die and advance care planning) and expected death experiences (e.g., palliative care, approaching dying, and bereavement). The specific alms of this Center application are to: (1) advance nursing science related to the end-of-life transition;(2) strengthen the ethical use of qualitative and quantitative (mixed methods) methodologies by Center investigators/affiliates and train novices regarding the ethical Issues in end of life research and mixed methods measurement of biobehavioral (biological, behavioral, and experiential) variables, management of data, and analysis of data;(3) expand the capacity to conduct end-of-life research with Informatics solutions that contribute to high integrity processes and efficient, valid, and reliable outcomes In Institutional and community-based settings;and (4) facilitate the dissemination of end-of-life research findings to the scientific and clinical communities and to the general public. Studies supported by the Center will be designed to: (a) elucidate underlying physiological, experiential, and behavioral mechanisms that prognosticate or influence approaching death or bereavement;and (b) test culturally relevant interventions for death awareness and respectful death. The research supported by the Center will have a biobehavioral orientation and will Include end-of life-care concepts that are measured physiologically (biologic function), experientially (symptoms, perceived health, quality of life), and behaviorally (functional status). The revision application adds to the Nursing and Healthcare Informatics Core by adding capacity for multimedia data analysis and visualization and data mining with substantial added expertise from faculty from the College of Engineering.